01Patient information sits under the Health Information Privacy Code. Sending it to a vendor-hosted model is often a non-starter before the conversation even begins.
Health providers hold some of the most tightly regulated data in the country, and the failure modes are serious. A private AI instance lets clinical and administrative teams search approved protocols, summarise case context and draft first-pass responses — while patient records stay inside your own environment and a clinician owns every decision that matters. Nothing is routed to a shared cloud, and the boundaries of what the AI can see and do are agreed before deployment.
Sovereign AI for healthcare & health services →
02Client confidentiality and legal privilege don’t bend for convenience. Privileged material can’t sit on infrastructure you don’t govern.
For a law firm, confidentiality isn’t a setting — it’s the obligation the whole practice rests on. A private AI instance can search across matters, surface relevant precedents and draft first passes, all inside a boundary you control, so privileged material never leaves infrastructure you govern. You decide what the model can access, and every output stays reviewable by the lawyer accountable for it.
Sovereign AI for law firms →
03Client financial data carries FMA and privacy obligations — and trust is the whole product, not a feature of it.
Advisers and accountants live and die on client trust, and the data they hold is both sensitive and regulated. A private AI instance can work over your own client files, advice records and internal knowledge — giving fast, consistent support to your team — while keeping that data inside your environment, with auditable boundaries and predictable, non-metered running costs instead of a bill that grows with every query.
Sovereign AI for financial advisers & accounting →
04Public records, citizen data and a standing duty to keep information in New Zealand. Data residency isn’t a preference here — it’s the rule.
Councils are accountable to the public for how citizen data is held and where it lives. A private AI instance can help staff navigate policy, respond to routine enquiries and search across public records — with a hard guarantee of where the data physically resides, full governance over what the system can do, and a human owning every decision that carries public consequence.
Sovereign AI for local government & councils →
05Māori data sovereignty is about who holds, governs and benefits from the data — not only where it’s stored. That principle comes first.
Māori data sovereignty is a question of authority and relationship, not just server location — who holds the data, who governs its use, and who benefits. The principles here are Māori-led: articulated by Te Mana Raraunga (the Māori Data Sovereignty Network) and echoed internationally in the CARE Principles for Indigenous Data Governance. We defer to that leadership. We approach any engagement as partnership first: any deployment sits inside your environment, under your governance and kaitiakitanga, so the data and the decisions about it remain with you. We start by listening, not by selling infrastructure.
Sovereign AI for māori organisations & iwi →
06Tenders, designs and commercial IP are competitive assets. Handing them to a third-party model hands them to everyone’s model.
Engineering and construction firms carry hard-won commercial knowledge — standards, designs, past tenders — that’s a competitive asset precisely because it’s yours. A private AI instance lets your teams search and reuse that knowledge in the moment, speeding up bids and reducing rework, while keeping every design and commercial document inside your own infrastructure rather than a vendor’s shared platform.
Sovereign AI for engineering & construction →
07Formulations, processes and production data are the business. Routing them through a shared cloud model exposes what took decades to build.
A manufacturer’s real moat is process knowledge: formulations, tolerances, machine settings, quality history, and the experience in operators’ heads. A private AI instance makes that knowledge searchable and usable at the point of work — troubleshooting, quality investigations, procedure lookups — while production data and IP stay inside infrastructure you control. It also keeps a production-adjacent system independent of an offshore vendor’s uptime and update schedule.
Sovereign AI for manufacturing →
08Rates, contracts and customer volumes are commercially sensitive — and margins are too thin to absorb usage-metered AI pricing at operational volume.
Logistics runs on high-volume, time-critical questions: what the contract allows, which rate applies, how an exception gets handled, where a consignment stands. A private AI instance answers those from your own contracts, rate cards, procedures and correspondence — instantly and consistently — while commercially sensitive customer and pricing data stays inside your environment. And because operational volume is exactly where usage-metered AI pricing punishes you, fixed-cost private deployment fits the economics of the sector.
Sovereign AI for logistics & supply chain →
09Your product is knowledge and your obligation is confidentiality — the two things public AI platforms handle worst.
Consultancies, agencies, engineers, advisers — professional services firms sell judgement built on accumulated knowledge, delivered under confidentiality. A private AI instance puts that accumulated knowledge to work: past engagements, methodologies, templates and research become searchable and reusable, proposals and deliverables get grounded first drafts, and client material never leaves infrastructure the firm governs. The billable-hour maths does the rest.
Sovereign AI for professional services →
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